Variation in Articulation Rate in New Brunswick French
Bibliographic record
Abstract
This study examines articulation rate (AR) in French spoken in five regions of New Brunswick. French is a minority language in this province, and the demographic concentrations of French speakers vary across regions, suggesting that the regions have different degrees of French–English contact. The main research question explored in this paper is how the different language contact situations are related to AR. Earlier research on contact varieties of various languages has shown that AR tends to be slower in regions where there are greater degrees of language contact ([1–4]). The present study also includes consideration of other factors that can affect AR variation: speaker gender and age, and length of inter-pause intervals (IPIs) ([5–7]).Speech data are from the RACAD speech corpus of New Brunswick Acadian French, originally designed for speech recognition applications. Analyzed in this study are two ‘calibration’ sentences that were read by all 136 participants. The sample size is well-balanced with a good distribution of gender and age for all five regions. Acoustic labeling – phones, syllables, pauses – was carried out with Praat ([8]). AR was calculated – locally per speaker – as the number of syllables in an IPI (and measured in syllables per second).Linear mixed-effects modeling shows that the region factor is not significant, that is, ARs did not differ across the different contact situations. This result is discussed in terms of earlier research on prosodic variation in contact varieties. Nevertheless, the effects of other factors are significant and are consistent with previous research. With respect to gender, males read the sentences faster than females. In the case of the age factor, AR decreased with speaker age. These findings contribute to a description of the temporal properties of contact varieties of Canadian French, an area that remains relatively under-documented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".